Feature  ·  Alerts & Incidents

Get paged before your users notice.

Alert rules on error rate, latency, cost, and eval quality. Multi-channel notifications. Full incident lifecycle with AI-generated postmortems.

Email, Slack, PagerDuty, webhook. Incident state machine. AI postmortem drafts.

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Zespan Alerts & Incidents
Works withEmailSlackPagerDutyWebhookEval-based alertsAI postmortems

5

incident states

3

alert metric targets

4

channels

1.0  Alerts & Incidents

Alert Rules

What you get

Metrics: error_rate, avg_latency, total_costConditions: >, <, >=, <=, ==, != with configurable windowMinEval metric alerts: link to any evaluation metric key for quality-based alerting

Alert rules fire when a metric crosses a threshold in a configurable window. Target error_rate, avg_latency, or total_cost. An optional comparison window enables week-over-week spike detection. Link alerts to evaluation metric keys — get paged when quality drops, not just when errors spike.

Zespan alert rules with metric, condition, window, and notification channel config

2.0  Alerts & Incidents

Multi-Channel Notifications

What you get

Email, webhook, Slack, PagerDuty — combine channels per ruleAlert history: triggered, resolved, acknowledged, config changesSensitive field redaction: email addresses and webhook URLs redacted in history

When an alert fires, notify via email (list of addresses), webhook (POST with payload), Slack, or PagerDuty. Mix channels per alert rule. Full alert history with sensitive fields (email addresses, webhook URLs) redacted.

3.0  Alerts & Incidents

Incident Lifecycle

What you get

States: open, investigating, mitigating, mitigated, resolvedTransitions: ACKNOWLEDGE, MITIGATE, CONFIRM_MITIGATION, REVERT, RESOLVE, REOPEN, ESCALATEAI correlation: background worker clusters related alerts and traces automatically

Incidents progress through a formal state machine: open → investigating → mitigating → mitigated → resolved. Severity levels (critical/high/medium/low) for triage. A background worker correlates related alerts and traces into incident candidates automatically.

Zespan incident management showing state machine, severity, and correlated traces

4.0  Alerts & Incidents

AI Postmortem Generation

What you get

AI draft from incident timeline and related tracesResolution documentation: type, notes, and ticket URLActive count badge: overview dashboard shows open + investigating incidents

Every resolved incident can have a postmortem document. Zespan generates an AI-assisted draft from the incident timeline and related traces — what happened, when, which agents were involved, and how it was resolved. Editable and persistent at /incidents/[id]/postmortem.

Setup

Under 5 minutes,
two lines of code.

No forking and no architecture changes. Traces appear within seconds of the first agent run, with cost attribution, eval scores, and anomaly alerts on by default.

typescript
// Alert rules are configured in the dashboard — no SDK code required.
// To trigger alerts from your own code, use the API:

await fetch('https://zespan.com/api/alerts', {
  method: 'POST',
  headers: { 'x-api-key': process.env.ZESPAN_API_KEY },
  body: JSON.stringify({
    metric: 'error_rate',
    condition: '>',
    threshold: 0.05,
    windowMin: 15,
    channels: ['slack', 'pagerduty'],
  }),
});

Common questions

Can I alert on output quality — not just error rate?

Yes. Link an alert rule to any evaluation metric key — e.g., 'faithfulness'. When the average faithfulness score for a time window drops below your threshold, Zespan fires the alert exactly like an error_rate alert. This is the only LLM monitoring platform that supports eval-based alerting natively.

What's the minimum alert window I can configure?

The windowMin parameter accepts any positive integer (minutes). There's no enforced minimum — you can configure a 1-minute window for very short-cycle checks. In practice, 5–15 minutes balances sensitivity with noise reduction.

How is AI correlation different from manual incident creation?

Manual incidents require someone to notice a problem and create the incident. AI correlation runs a background worker continuously that clusters related alerts and trace anomalies into incident candidates automatically — so the incident exists before you've even looked at dashboards.

Can I integrate Zespan alerts with my existing on-call rotation?

Yes. PagerDuty integration dispatches to your existing services and schedules. Webhook integration lets you push to any system — Opsgenie, VictorOps, a custom Slack app, or your own incident management tooling.

Explore more features

All features →
TracingSee exactly what your agents are doing.Every LLM call, agent step, and tool invocation captured as a structured trace — with cost, latency, and tokens per span.Agent MonitoringKnow which agents are healthy, and which aren't.Composite health scores, delegation graphs, and per-agent cost attribution — built for systems with many cooperating AI agents.EvaluationsMeasure output quality on every trace, automatically.12 built-in LLM-as-judge templates run on every new trace with no setup. Track quality trends, catch regressions, and run manual eval campaigns.GuardrailsStop bad outputs before they reach users.7 guardrail types run inline on every LLM request — block, warn, redact, or log. PII, toxicity, topic drift, format, cost ceiling, and custom rules.

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